Pandas: generate and display medium

Question

I have pandas dataframe like:

In [61]: df = DataFrame(np.random.rand(3,4), index=['art','mcf','mesa'], columns=['pol1','pol2','pol3','pol4']) In [62]: df Out[62]: pol1 pol2 pol3 pol4 art 0.661592 0.479202 0.700451 0.345085 mcf 0.235517 0.665981 0.778774 0.610344 mesa 0.838396 0.035648 0.424047 0.866920 

and I want to create a line with the average value for the policies in the tests, and then build it.

Currently, the way I do this is:

 df = df.T df['average'] = df.apply(average, axis=1) df = df.T df.plot(kind='bar') 

Is there an elegant way to avoid double transposition?

I tried:

 df.append(DataFrame(df.apply(average)).T) df.plot(kind='bar') 

This will add the correct values, but will not update the index correctly, and the graph is corrupted.

Clarification. The result of the double transposition code is the following: This is what I want. To show both benchmarks and average policies, not just average. I was just curious if I could do it better.

Note that the legend is usually confused. For correction:

 ax = df.plot(kind='bar') ax.legend(patches, list(df.columns), loc='best') 

Answer 1

You can simply use the mean instance method for the DataFrame , rather than building the results. No need for transposition.

 In [14]: df.mean() Out[14]: pol1 0.578502 pol2 0.393610 pol3 0.634424 pol4 0.607450 In [15]: df.mean().plot(kind='bar') Out[15]: <matplotlib.axes.AxesSubplot at 0x4a327d0> 

Update

If you want to build the rows of all columns, and the average value of append means:

 In [95]: average = df.mean() In [96]: average.name = 'average' In [97]: df = df.append(average) In [98]: df Out[98]: pol1 pol2 pol3 pol4 art 0.661592 0.479202 0.700451 0.345085 mcf 0.235517 0.665981 0.778774 0.610344 mesa 0.838396 0.035648 0.424047 0.866920 average 0.578502 0.393610 0.634424 0.607450 In [99]: df.plot(kind='bar') Out[99]: <matplotlib.axes.AxesSubplot at 0x52f4390> 

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